Human-Inspired Robotic Eye-Hand Coordination Enables New Communication Channels Between Humans and Robots

Olson, S.A.; Moaed, A.; Engeberg, Erik D.

doi:10.1007/s12369-020-00693-2

Cited by 2 publications

(4 citation statements)

References 36 publications

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“…However, sometimes the information expected to be expressed by head movement is not perfect and the interacting objects cannot understand the full meaning, and then the auxiliary functions of other limbs become extremely important. Researchers have explored the role of coordination movement of different limbs (e.g., hand-eye coordination ( Chao et al, 2018 ; Olson et al, 2020 ), head-eye coordination ( Omrcen and Ude, 2010 ; Milighetti et al, 2011 ), neck-eye coordination ( Rajruangrabin and Popa, 2010 ), etc.) in augmenting head movement at the level of information conveyance, as well as their planning and control schemes.…”

Section: Motion Coordinationmentioning

confidence: 99%

Anthropomorphic motion planning for multi-degree-of-freedom arms

Zheng,

Han,

Liang

2024

Front. Bioeng. Biotechnol.

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With the development of technology, the humanoid robot is no longer a concept, but a practical partner with the potential to assist people in industry, healthcare and other daily scenarios. The basis for the success of humanoid robots is not only their appearance, but more importantly their anthropomorphic behaviors, which is crucial for the human-robot interaction. Conventionally, robots are designed to follow meticulously calculated and planned trajectories, which typically rely on predefined algorithms and models, resulting in the inadaptability to unknown environments. Especially when faced with the increasing demand for personalized and customized services, predefined motion planning cannot be adapted in time to adapt to personal behavior. To solve this problem, anthropomorphic motion planning has become the focus of recent research with advances in biomechanics, neurophysiology, and exercise physiology which deepened the understanding of the body for generating and controlling movement. However, there is still no consensus on the criteria by which anthropomorphic motion is accurately generated and how to generate anthropomorphic motion. Although there are articles that provide an overview of anthropomorphic motion planning such as sampling-based, optimization-based, mimicry-based, and other methods, these methods differ only in the nature of the planning algorithms and have not yet been systematically discussed in terms of the basis for extracting upper limb motion characteristics. To better address the problem of anthropomorphic motion planning, the key milestones and most recent literature have been collated and summarized, and three crucial topics are proposed to achieve anthropomorphic motion, which are motion redundancy, motion variation, and motion coordination. The three characteristics are interrelated and interdependent, posing the challenge for anthropomorphic motion planning system. To provide some insights for the research on anthropomorphic motion planning, and improve the anthropomorphic motion ability, this article proposes a new taxonomy based on physiology, and a more complete system of anthropomorphic motion planning by providing a detailed overview of the existing methods and their contributions.

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Section: Motion Coordinationmentioning

confidence: 99%

Anthropomorphic motion planning for multi-degree-of-freedom arms

Zheng,

Han,

Liang

2024

Front. Bioeng. Biotechnol.

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“…Connected IoRT mobile devices and collaborative robots gather and sense data from the surrounding environment across fog and cloud computing networks [57][58][59][60] through computation and storage capabilities. Robotic operating systems can perform tasks autonomously by use of situational and contextual awareness capabilities in relation to interactive and collaborative tasks in smart environments.…”

Section: Sensing and Computing Technologies In The Internet Of Roboti...mentioning

confidence: 99%

“…[ [53][54][55][56][57][58][59][60] Image processing and visual modeling tools, edge surveillance and computing technologies, and deep neural networks optimize robotic agent behaviors in uncontrolled environments. Modeling and simulation tools, scheduling algorithms and decisions, and motion planning algorithms optimize cyber-physical and robotic systems.…”

Section: Table 4 Synopsis Of Evidence As Regards Analyzed Topics and ...mentioning

confidence: 99%

“…Data mining and context recognition tools enhance sensing and actuation capabilities of robotic networks [49][50][51][52] as autonomous agents. Smart devices, context aware systems, and artificial neural networks [53][54][55][56] further continuous sensing and contextual data monitoring [57][58][59][60] across the ambient environment and spatial surroundings. Robotic operating and autonomous control systems are pivotal in reconfigurable manufacturing processes [61][62][63][64] in Industry 4.0-based networked environments.…”

Section: Introductionmentioning

confidence: 99%

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Remote Big Data Management Tools, Sensing and Computing Technologies, and Visual Perception and Environment Mapping Algorithms in the Internet of Robotic Things

et al. 2022

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The purpose of our systematic review was to inspect the recently published research on Internet of Robotic Things (IoRT) and harmonize the assimilations it articulates on remote big data management tools, sensing and computing technologies, and visual perception and environment mapping algorithms. The research problems were whether robotic manufacturing processes and industrial wireless sensor networks shape IoRT and lead to improved product quality by use of remote big data management tools, whether IoRT devices communicate autonomously regarding event modeling and forecasting by leveraging machine learning and clustering algorithms, sensing and computing technologies, and image processing tools, and whether smart connected objects, situational awareness algorithms, and edge computing technologies configure IoRT systems and cloud robotics in relation to distributed task coordination through visual perception and environment mapping algorithms. A Shiny app was harnessed for Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) guidelines to configure the flow diagram integrating evidence-based gathered and processed data (the search outcomes and screening procedures). A quantitative literature review of ProQuest, Scopus, and the Web of Science databases was carried out throughout June and October 2022, with search terms including “Internet of Robotic Things” + “remote big data management tools”, “sensing and computing technologies”, and “visual perception and environment mapping algorithms”. Artificial intelligence and intelligent workflows by use of AMSTAR (Assessing the Methodological Quality of Systematic Reviews), Dedoose, DistillerSR, and SRDR (Systematic Review Data Repository) have been deployed as data extraction tools for literature collection, screening, and evaluation, for document flow monitoring, for inspecting qualitative and mixed methods research, and for establishing robust outcomes and correlations. For bibliometric mapping by use of data visualization, Dimensions AI was leveraged and with regards to layout algorithms, VOSviewer was harnessed.

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Human-Inspired Robotic Eye-Hand Coordination Enables New Communication Channels Between Humans and Robots

Cited by 2 publications

References 36 publications

Anthropomorphic motion planning for multi-degree-of-freedom arms

Anthropomorphic motion planning for multi-degree-of-freedom arms

Remote Big Data Management Tools, Sensing and Computing Technologies, and Visual Perception and Environment Mapping Algorithms in the Internet of Robotic Things

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